Click here for interactive plots:

https://kforthman.shinyapps.io/COVID19_Interactive_Plots/

Click here to see how cities and counties overlap:

https://kforthman.shinyapps.io/500citiescounties

Compare COVID stats to Neighborhood Factors

data.cor <- cor(county.Demo_and_Covid.allcounties[,-1], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

Compare COVID stats to 500 cities data and Neighborhood Factors

data.cor2 <- cor(county.Demo_and_Covid.500counties[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor2, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

corrplot.mixed(data.cor2[7:13,c(1:5, 14:42,6)], upper = 'ellipse', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

—-Linear Mixed Effects Model —-

this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)", data = county.Demo_and_Covid.500counties)
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -1189.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9747 -0.3562 -0.0937  0.1800  5.5856 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000001378 0.001174
##  Residual             0.000014342 0.003787
## Number of obs: 178, groups:  stateID, 33
## 
## Fixed effects:
##                                  Estimate     Std. Error             df
## (Intercept)                 -0.0104117203   0.0098432057  68.8282759388
## Affluence                    0.0048274431   0.0011309123  99.1979648622
## Singletons.in.Tract          0.0014866677   0.0009340002 139.6332119796
## Seniors.in.Tract             0.0009346562   0.0012289519 149.4996429189
## African.Americans.in.Tract   0.0006462671   0.0010311797 151.4729127508
## Noncitizens.in.Tract         0.0009516998   0.0007940877 124.7855041345
## High.BP                      0.0001913624   0.0001937876 107.6902419495
## Binge.Drinking               0.0001619004   0.0001617185  41.9215247865
## Cancer                      -0.0009864808   0.0011305537  99.6552434308
## Asthma                       0.0006856561   0.0005711476  40.5575488973
## Heart.Disease                0.0011807170   0.0013408495  72.1646517163
## COPD                        -0.0001521381   0.0011122050  75.3294730652
## Smoking                     -0.0000977564   0.0002324652  78.4332543214
## Diabetes                    -0.0005905303   0.0005481730  78.9237172176
## No.Physical.Activity        -0.0000217046   0.0002104448  88.2761580675
## Obesity                      0.0002522799   0.0001816996  96.9451317512
## Poor.Sleeping.Habits        -0.0000040978   0.0001694813 122.6925612117
## Poor.Mental.Health          -0.0000681915   0.0004266173  30.7879875558
## Testing_Rate                 0.0000005219   0.0000002714  33.8357903501
## Hospitalization_Rate        -0.0001100855   0.0000925599  27.6921970680
##                            t value  Pr(>|t|)    
## (Intercept)                 -1.058     0.294    
## Affluence                    4.269 0.0000451 ***
## Singletons.in.Tract          1.592     0.114    
## Seniors.in.Tract             0.761     0.448    
## African.Americans.in.Tract   0.627     0.532    
## Noncitizens.in.Tract         1.198     0.233    
## High.BP                      0.987     0.326    
## Binge.Drinking               1.001     0.323    
## Cancer                      -0.873     0.385    
## Asthma                       1.200     0.237    
## Heart.Disease                0.881     0.381    
## COPD                        -0.137     0.892    
## Smoking                     -0.421     0.675    
## Diabetes                    -1.077     0.285    
## No.Physical.Activity        -0.103     0.918    
## Obesity                      1.388     0.168    
## Poor.Sleeping.Habits        -0.024     0.981    
## Poor.Mental.Health          -0.160     0.874    
## Testing_Rate                 1.923     0.063 .  
## Hospitalization_Rate        -1.189     0.244    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence    0.135                                                        
## Sngltns.n.T  0.021  0.073                                                 
## Snrs.n.Trct  0.571  0.377  0.192                                          
## Afrcn.Am..T  0.158  0.150 -0.409  0.141                                   
## Nnctzns.n.T  0.000  0.100  0.041  0.065 -0.079                            
## High.BP     -0.006  0.239  0.063  0.110 -0.095  0.396                     
## Bing.Drnkng -0.279 -0.195 -0.297 -0.185  0.078  0.038  0.133              
## Cancer      -0.588 -0.199  0.180 -0.330 -0.074 -0.145 -0.379 -0.115       
## Asthma      -0.370 -0.213 -0.239 -0.193  0.088  0.091  0.170 -0.010  0.046
## Heart.Dises -0.150  0.077 -0.290 -0.153  0.248 -0.105 -0.012  0.057 -0.470
## COPD         0.566  0.040  0.138  0.284 -0.007  0.286  0.179  0.114 -0.272
## Smoking     -0.175  0.145 -0.169 -0.104 -0.059  0.003 -0.068 -0.297  0.093
## Diabetes     0.075 -0.336 -0.104 -0.222 -0.308 -0.323 -0.525  0.049  0.242
## N.Physcl.Ac -0.178 -0.058  0.076 -0.040 -0.035 -0.225 -0.113  0.100  0.478
## Obesity      0.004  0.427  0.422  0.303  0.143  0.198 -0.088 -0.237  0.110
## Pr.Slpng.Hb -0.453 -0.405  0.143 -0.361 -0.358 -0.021 -0.189  0.092  0.140
## Pr.Mntl.Hlt -0.334  0.257 -0.063 -0.070  0.100 -0.182 -0.077  0.060  0.314
## Testing_Rat  0.173 -0.065 -0.018  0.032  0.044 -0.083 -0.014  0.019 -0.176
## Hsptlztn_Rt -0.155 -0.206 -0.112 -0.224 -0.050 -0.113 -0.120 -0.136  0.058
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H Pr.M.H
## Affluence                                                                 
## Sngltns.n.T                                                               
## Snrs.n.Trct                                                               
## Afrcn.Am..T                                                               
## Nnctzns.n.T                                                               
## High.BP                                                                   
## Bing.Drnkng                                                               
## Cancer                                                                    
## Asthma                                                                    
## Heart.Dises  0.280                                                        
## COPD        -0.380 -0.562                                                 
## Smoking      0.086  0.206 -0.518                                          
## Diabetes    -0.120 -0.291 -0.106  0.233                                   
## N.Physcl.Ac  0.010 -0.382 -0.011 -0.324 -0.060                            
## Obesity     -0.275 -0.095  0.165 -0.200 -0.392 -0.065                     
## Pr.Slpng.Hb  0.066  0.250 -0.201  0.005 -0.011 -0.117 -0.167              
## Pr.Mntl.Hlt -0.241  0.087 -0.445  0.089  0.028  0.050  0.094 -0.188       
## Testing_Rat -0.353 -0.039  0.193  0.133  0.109 -0.302  0.109 -0.108 -0.086
## Hsptlztn_Rt  0.080  0.093 -0.128  0.090  0.072 -0.026 -0.036  0.003 -0.040
##             Tstn_R
## Affluence         
## Sngltns.n.T       
## Snrs.n.Trct       
## Afrcn.Am..T       
## Nnctzns.n.T       
## High.BP           
## Bing.Drnkng       
## Cancer            
## Asthma            
## Heart.Dises       
## COPD              
## Smoking           
## Diabetes          
## N.Physcl.Ac       
## Obesity           
## Pr.Slpng.Hb       
## Pr.Mntl.Hlt       
## Testing_Rat       
## Hsptlztn_Rt  0.190
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)", data = county.Demo_and_Covid.500counties)

print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -2477.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7177 -0.3398 -0.0885  0.2272  6.5945 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000006569 0.002563
##  Residual             0.000011304 0.003362
## Number of obs: 326, groups:  stateID, 51
## 
## Fixed effects:
##                                Estimate   Std. Error           df t value
## (Intercept)                 -0.01972520   0.00751320 190.37930648  -2.625
## Affluence                    0.00279058   0.00068406 301.56986210   4.079
## Singletons.in.Tract          0.00118468   0.00063955 301.46059112   1.852
## Seniors.in.Tract             0.00064501   0.00080763 304.73321981   0.799
## African.Americans.in.Tract   0.00143834   0.00078051 306.98596092   1.843
## Noncitizens.in.Tract         0.00159725   0.00062846 269.63362561   2.542
## High.BP                     -0.00003444   0.00014113 297.63541521  -0.244
## Binge.Drinking               0.00036333   0.00014778 155.93263523   2.459
## Cancer                      -0.00035104   0.00082698 264.19602838  -0.424
## Asthma                       0.00049824   0.00048958 138.91100871   1.018
## Heart.Disease                0.00287246   0.00105962 207.16002099   2.711
## COPD                        -0.00113448   0.00080204 201.92101740  -1.414
## Smoking                     -0.00023756   0.00018559 247.58463762  -1.280
## Diabetes                    -0.00099714   0.00039791 266.75680391  -2.506
## No.Physical.Activity         0.00025557   0.00015971 234.55514903   1.600
## Obesity                      0.00021835   0.00012965 307.99766654   1.684
## Poor.Sleeping.Habits         0.00027737   0.00012472 296.48269662   2.224
## Poor.Mental.Health          -0.00012057   0.00041478 101.67913458  -0.291
##                             Pr(>|t|)    
## (Intercept)                  0.00936 ** 
## Affluence                  0.0000578 ***
## Singletons.in.Tract          0.06495 .  
## Seniors.in.Tract             0.42512    
## African.Americans.in.Tract   0.06632 .  
## Noncitizens.in.Tract         0.01160 *  
## High.BP                      0.80737    
## Binge.Drinking               0.01504 *  
## Cancer                       0.67156    
## Asthma                       0.31059    
## Heart.Disease                0.00727 ** 
## COPD                         0.15876    
## Smoking                      0.20174    
## Diabetes                     0.01281 *  
## No.Physical.Activity         0.11091    
## Obesity                      0.09318 .  
## Poor.Sleeping.Habits         0.02691 *  
## Poor.Mental.Health           0.77188    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence   -0.047                                                        
## Sngltns.n.T -0.057  0.044                                                 
## Snrs.n.Trct  0.398  0.293  0.074                                          
## Afrcn.Am..T  0.242  0.076 -0.405  0.202                                   
## Nnctzns.n.T -0.072  0.153  0.126  0.057 -0.189                            
## High.BP     -0.096  0.157  0.099  0.007 -0.235  0.329                     
## Bing.Drnkng -0.486 -0.043 -0.206 -0.070  0.042 -0.076  0.149              
## Cancer      -0.496 -0.096  0.231 -0.174 -0.073 -0.068 -0.329 -0.021       
## Asthma      -0.267 -0.098 -0.262 -0.120 -0.012  0.210  0.054  0.007 -0.158
## Heart.Dises -0.057  0.076 -0.300 -0.132  0.212 -0.054 -0.002  0.034 -0.602
## COPD         0.479  0.012  0.127  0.173 -0.004  0.156  0.059  0.061 -0.213
## Smoking     -0.045  0.105 -0.119 -0.137 -0.105  0.160 -0.083 -0.327  0.158
## Diabetes     0.036 -0.300 -0.079 -0.133 -0.230 -0.255 -0.445  0.075  0.365
## N.Physcl.Ac -0.115  0.033  0.101  0.079  0.060 -0.274  0.004  0.124  0.338
## Obesity     -0.065  0.384  0.398  0.203  0.133  0.194 -0.103 -0.149  0.119
## Pr.Slpng.Hb -0.386 -0.352  0.163 -0.327 -0.322 -0.046 -0.156  0.087  0.029
## Pr.Mntl.Hlt -0.354  0.183 -0.007  0.019  0.050 -0.166  0.026  0.131  0.417
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H
## Affluence                                                          
## Sngltns.n.T                                                        
## Snrs.n.Trct                                                        
## Afrcn.Am..T                                                        
## Nnctzns.n.T                                                        
## High.BP                                                            
## Bing.Drnkng                                                        
## Cancer                                                             
## Asthma                                                             
## Heart.Dises  0.336                                                 
## COPD        -0.323 -0.490                                          
## Smoking      0.144  0.082 -0.476                                   
## Diabetes    -0.106 -0.430 -0.010  0.278                            
## N.Physcl.Ac -0.024 -0.361  0.087 -0.274 -0.169                     
## Obesity     -0.128 -0.021  0.092 -0.220 -0.377 -0.045              
## Pr.Slpng.Hb  0.000  0.240 -0.094 -0.166 -0.060 -0.154 -0.115       
## Pr.Mntl.Hlt -0.436 -0.067 -0.388 -0.027  0.072 -0.083  0.027 -0.082

Testing Rate

testing.data.state <- compiled.stats[[length(daily_filenames)]][, c("Province_State", "Testing_Rate")]
testing.data.state <- testing.data.state[!is.na(testing.data.state$Testing_Rate),]
testing.data.state <- testing.data.state[order(testing.data.state$Testing_Rate),]

col.state <- rep("pink", nrow(testing.data.state))

avg.test.rate <- mean(testing.data.state$Testing_Rate, na.rm = T)

col.state[testing.data.state$Testing_Rate < avg.test.rate] <- "grey"
col.state[testing.data.state$Province_State == "Oklahoma"] <- "lightblue"

par(mar = c(5,6,4,2))
barplot(testing.data.state$Testing_Rate, names.arg = testing.data.state$Province_State, horiz = T, main = "Testing Rate by State", las = 2, cex.axis = 1, cex.names = 0.5, col = col.state, border = F, xlab = "Total number of people tested per 100,000 persons.")
abline(v = avg.test.rate, col = "red")
text(x = avg.test.rate + 10, y = 1, labels = "Average Testing Rate", adj = c(0, 0.5), col = "red")

Pink highlights the last 14 days.

day.first.case <- min(which(US.total$cases.total > 100))
n.days <- nrow(US.total)

twoweek.col <- c(rep("grey", n.days-day.first.case-13), rep("pink", 14))

par(mar = c(5,5,4,2))
barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$rise.cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Cases of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$rise.deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Deaths of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)